Conventionally, a technique for removing noise components from a digital image on which noise components that are not contained in the original signal components are superposed has been studied. The characteristics of noise to be removed are diverse depending on their generation factors, and noise removal methods suited to those characteristics have been proposed.
For example, when an image input device such as a digital camera, image scanner, or the like is assumed, noise components are roughly categorized into noise which depends on the input device characteristics of a solid-state image sensing element or the like and input conditions such as an image sensing mode, scene, or the like, and has already been superposed on a photoelectrically converted analog original signal, and noise which is superposed via various digital signal processes after the analog signal is converted into a digital signal via an A/D converter.
As an example of the former (noise superposed on an analog signal), impulse noise that generates an isolated value to have no correlation with surrounding image signal values, noise resulting from the dark current of the solid-state image sensing element, and the like are known. As an example of the latter (noise superposed during a digital signal process), noise components are amplified simultaneously with signal components when a specific density, color, and the like are emphasized in various correction processes such as gamma correction, gain correction for improving the sensitivity, and the like, thus increasing the noise level.
As an example of deterioration of an image due to noise superposed in a digital signal process, since an encoding process using a JPEG algorithm extracts a plurality of blocks from two-dimensional (2D) image information, and executes orthogonal transformation and quantization for respective blocks, a decoded image suffers block distortion that generates steps at the boundaries of blocks.
In addition to various kinds of noise mentioned above, a factor that especially impairs the image quality is noise (to be referred to as “low-frequency noise” hereinafter) which is generated in a low-frequency range and is conspicuously observed in an image sensed by a digital camera or the like. This low-frequency noise often results from the sensitivity of a CCD or CMOS sensor as a solid-state image sensing element. In an image sensing scene such as a dark scene with a low signal level, a shadowy scene, or the like, low-frequency noise is often emphasized due to gain correction that raises signal components irrespective of poor S/N ratio.
Furthermore, the element sensitivity of the solid-state image sensing element depends on its chip area. Hence, in a digital camera which has a large number of pixels within a small area, the amount of light per unit pixel consequently decreases, and the sensitivity lowers, thus producing low-frequency noise. For example, low-frequency noise is often visually recognized as pseudo mottled texture across several to ten-odd pixels on a portion such as a sheet of blue sky or the like which scarcely has any change in density (to be referred to as a “flat portion” hereinafter). Some digital cameras often produce false colors.
As a conventionally proposed noise removal method, a method using a median filter (to be abbreviated as “MF” hereinafter) and a method using a low-pass filter (to be abbreviated as “LPF” hereinafter) that passes only a low-frequency range have prevailed.
The noise removal method using an MF removes impulse noise by extracting a pixel value which assumes a median from those of a pixel of interest and its surrounding pixels, and replacing the pixel value of interest by the extracted value. The noise removal method using an LPF is effective for impulse noise, block distortion mentioned above, and the like, and removes noise by calculating the weighted mean using a pixel value of interest and its surrounding pixel values, and replacing the pixel value of interest by the calculated weighted mean.
On the other hand, as a method effective for low-frequency noise, a method of replacing a pixel value of interest by a pixel value which is probabilistically selected from those around the pixel of interest (to be referred to as a “noise distribution method” hereinafter) has been proposed.
A conventional process for removing noise superposed on image information is done while balancing the effects of the aforementioned noise removal process and the adverse effects produced by these processes, i.e., within a range in which sufficient effects are recognized and the degree of adverse effects is allowed.
When digital image information is to be displayed on a display, its resolution can be changed to various values at the time of display using application software or the like. Also, digital image information can be printed using a printer in an enlarged or reduced scale.
However, whether or not the adverse effects due to removal of noise superposed on image information are visually recognized largely depends on the resolution of image information. For example, as a feature of the adverse effect of an LPF, an image blurs. As one factor for determining the degree of production of such image blur, a window size is known. However, when the window size is fixed, the window size is uniquely determined with respect to the number of pixels, but the size upon referring to pixels in practice is determined by the number of pixels and resolution. For this reason, a blur as an adverse effect of an LPF is visually recognized depending on the resolution of image information.